Merge master into GH13936

Author: Christopher C. Aycock

.travis.yml

@@ -16,7 +16,7 @@ env:
  global:
 
  # pandas-docs-travis GH
- - secure: "PCzUFR8CHmw9lH84p4ygnojdF7Z8U5h7YfY0RyT+5K/aiQ1ZTU3ZkDTPI0/rR5FVMxsEEKEQKMcc5fvqW0PeD7Q2wRmluloKgT9w4EVEJ1ppKf7lITPcvZR2QgVOvjv4AfDtibLHFNiaSjzoqyJVjM4igjOu8WTlF3JfZcmOQjQ="
+ - secure: "YvvTc+FrSYHgdxqoxn9s8VOaCWjvZzlkaf6k55kkmQqCYR9dPiLMsot1F96/N7o3YlD1s0znPQCak93Du8HHi/8809zAXloTaMSZrWz4R4qn96xlZFRE88O/w/Z1t3VVYpKX3MHlCggBc8MtXrqmvWKJMAqXyysZ4TTzoiJDPvE="
 
 git:
  # for cloning

asv_bench/benchmarks/algorithms.py

@@ -3,7 +3,7 @@
 from pandas.util import testing as tm
 
 
-class algorithm(object):
+class Algorithms(object):
  goal_time = 0.2
 
  def setup(self):
@@ -24,21 +24,28 @@ def setup(self):
  self.arrneg = np.arange(-1000000, 0)
  self.arrmixed = np.array([1, -1]).repeat(500000)
 
- def time_int_factorize(self):
+ # match
+ self.uniques = tm.makeStringIndex(1000).values
+ self.all = self.uniques.repeat(10)
+
+ def time_factorize_int(self):
  self.int.factorize()
 
- def time_float_factorize(self):
+ def time_factorize_float(self):
  self.int.factorize()
 
- def time_int_unique_duplicated(self):
+ def time_duplicated_int_unique(self):
  self.int_unique.duplicated()
 
- def time_int_duplicated(self):
+ def time_duplicated_int(self):
  self.int.duplicated()
 
- def time_float_duplicated(self):
+ def time_duplicated_float(self):
  self.float.duplicated()
 
+ def time_match_strings(self):
+ pd.match(self.all, self.uniques)
+
  def time_add_overflow_pos_scalar(self):
  self.checked_add(self.arr, 1)
 
@@ -58,7 +65,7 @@ def time_add_overflow_mixed_arr(self):
  self.checked_add(self.arr, self.arrmixed)
 
 
-class hashing(object):
+class Hashing(object):
  goal_time = 0.2
 
  def setup(self):

asv_bench/benchmarks/attrs_caching.py

@@ -1,23 +1,32 @@
 from .pandas_vb_common import *
+from pandas.util.decorators import cache_readonly
 
 
-class getattr_dataframe_index(object):
+class DataFrameAttributes(object):
  goal_time = 0.2
 
  def setup(self):
  self.df = DataFrame(np.random.randn(10, 6))
  self.cur_index = self.df.index
 
- def time_getattr_dataframe_index(self):
+ def time_get_index(self):
  self.foo = self.df.index
 
+ def time_set_index(self):
+ self.df.index = self.cur_index
+
 
-class setattr_dataframe_index(object):
+class CacheReadonly(object):
  goal_time = 0.2
 
  def setup(self):
- self.df = DataFrame(np.random.randn(10, 6))
- self.cur_index = self.df.index
 
- def time_setattr_dataframe_index(self):
- self.df.index = self.cur_index
+ class Foo:
+
+ @cache_readonly
+ def prop(self):
+ return 5
+ self.obj = Foo()
+
+ def time_cache_readonly(self):
+ self.obj.prop